---
license: apache-2.0
base_model: WebScraper991923/Affine-S3
tags:
- qwen3
- affine
- game
- reinforcement-learning
- openspiel
---

# Affine-S3-GAME-Improved

Fine-tuned version of [WebScraper991923/Affine-S3](https://huggingface.co/WebScraper991923/Affine-S3) with improved GAME (OpenSpiel) performance for Bittensor Subnet 120 (Affine).

## Model Details

- **Base Model**: WebScraper991923/Affine-S3 (Qwen3-4B)
- **Training**: LoRA fine-tuning on 7,071 MCTS-generated game examples
- **Target**: Improved strategic game-playing for Affine evaluation

## Training Details

- **Method**: LoRA (r=32, alpha=32)
- **Data**: 7,071 examples from MCTS self-play across 9 games:
  - checkers (2,702 examples)
  - gin_rummy (1,896 examples)
  - othello (1,209 examples)
  - quoridor, phantom_ttt, hex, dots_and_boxes, leduc_poker, liars_dice
- **Epochs**: 2
- **Final Loss**: 0.024

## Performance

| Benchmark | Base Model | This Model |
|-----------|------------|------------|
| GAME Accuracy | ~30% | **76%** |
| LGC | 99.9% | 99.9% (preserved) |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("altro/Affine-S3-GAME", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("altro/Affine-S3-GAME")
```

## Affine Competition

This model is designed for Bittensor Subnet 120 (Affine), which rewards models that dominate the Pareto frontier across multiple RL evaluation tasks.